Meeting Title: Eden | Standup Date: 2025-09-11 Meeting participants: Awaish Kumar, Henry Zhao, Amber Lin
WEBVTT
1 00:01:54.710 ⇒ 00:01:56.100 Henry Zhao: I wish.
2 00:02:00.370 ⇒ 00:02:01.000 Awaish Kumar: Prodo.
3 00:03:56.270 ⇒ 00:03:57.310 Amber Lin: Hello!
4 00:04:12.340 ⇒ 00:04:14.949 Amber Lin: A lot of stuff gonna be here.
5 00:04:15.810 ⇒ 00:04:22.050 Amber Lin: So… Looking… ugh, jeezed.
6 00:04:22.820 ⇒ 00:04:26.240 Amber Lin: Oh, great, the… the other one’s got done.
7 00:04:26.580 ⇒ 00:04:30.979 Amber Lin: And then we’re waiting… we’re still waiting on Ryan’s response for GHL, right?
8 00:04:30.980 ⇒ 00:04:43.400 Awaish Kumar: I got the response from Brian, but I met him yesterday, but he then directed me towards another person called Joseph. So, basically, I haven’t got the answer yet, but we are in the call.
9 00:04:43.910 ⇒ 00:04:48.920 Awaish Kumar: So, I will… I’ll bring Joseph again today, and
10 00:04:49.300 ⇒ 00:04:51.699 Awaish Kumar: Try to resolve it this week.
11 00:04:52.170 ⇒ 00:04:53.450 Amber Lin: Oh, okay, fine.
12 00:04:54.140 ⇒ 00:04:58.010 Awaish Kumar: This is in progress, but it’s still blocked by stakeholder, because
13 00:04:58.210 ⇒ 00:05:00.860 Awaish Kumar: Now, Joseph has to give me the reply.
14 00:05:00.860 ⇒ 00:05:02.979 Amber Lin: Mmm. I see.
15 00:05:03.190 ⇒ 00:05:04.559 Amber Lin: I see, I see.
16 00:05:05.500 ⇒ 00:05:07.670 Amber Lin: Okay.
17 00:05:09.820 ⇒ 00:05:10.890 Amber Lin: Alright.
18 00:05:11.900 ⇒ 00:05:14.559 Amber Lin: And then, on…
19 00:05:15.190 ⇒ 00:05:20.859 Awaish Kumar: Demlade is also here, so is there anything urgent you want me to take?
20 00:05:21.710 ⇒ 00:05:22.740 Awaish Kumar: cover.
21 00:05:24.350 ⇒ 00:05:27.310 Amber Lin: Let’s check…
22 00:05:27.530 ⇒ 00:05:30.620 Henry Zhao: I guess maybe the modeling for the Tableau task I have?
23 00:05:31.510 ⇒ 00:05:41.659 Amber Lin: Yeah, where is that? He… Dude… so, essentially, the payroll…
24 00:05:42.500 ⇒ 00:05:48.940 Amber Lin: Data… let me check if he asked, finance…
25 00:05:52.940 ⇒ 00:05:54.180 Amber Lin: Boom.
26 00:06:09.480 ⇒ 00:06:16.179 Amber Lin: Yeah, I… because we need… we need this…
27 00:06:16.880 ⇒ 00:06:22.520 Amber Lin: So that we can build the…
28 00:06:23.650 ⇒ 00:06:29.290 Amber Lin: one… a part of the models, but I don’t know if he’s added the data yet.
29 00:06:32.390 ⇒ 00:06:38.230 Awaish Kumar: So… Are you able to download the data?
30 00:06:39.270 ⇒ 00:06:55.420 Amber Lin: Yes, we were able to download the aggregated data, so we don’t have the broken down data, and that’s what Demolina said yesterday. We can’t find it by transaction ID, so we only have the aggregation.
31 00:06:55.550 ⇒ 00:07:00.650 Awaish Kumar: Like, for the… Are you talking about payroll data, or something else?
32 00:07:01.000 ⇒ 00:07:05.790 Amber Lin: I think both payroll and chargebacks. So, for both payroll…
33 00:07:05.790 ⇒ 00:07:07.209 Awaish Kumar: Like, charge mix?
34 00:07:07.560 ⇒ 00:07:17.210 Awaish Kumar: Chargebacks basically goes back to the customer. That’s why maybe Gaminade wanted transaction ID. For payroll, we don’t need that, right?
35 00:07:17.600 ⇒ 00:07:18.650 Amber Lin: Yeah, yeah.
36 00:07:21.560 ⇒ 00:07:22.880 Amber Lin: That’s correct.
37 00:07:24.520 ⇒ 00:07:25.410 Awaish Kumar: Okay.
38 00:07:30.570 ⇒ 00:07:32.360 Amber Lin: So we could do something where…
39 00:07:32.360 ⇒ 00:07:32.850 Awaish Kumar: bitch.
40 00:07:32.850 ⇒ 00:07:33.380 Amber Lin: Okay, fine.
41 00:07:33.380 ⇒ 00:07:41.840 Awaish Kumar: Like, that’s a… question, like, how they, map chargebacks with the specific order, so…
42 00:07:42.490 ⇒ 00:07:45.430 Awaish Kumar: I will bump that message again today, maybe we get an answer.
43 00:07:45.930 ⇒ 00:07:55.250 Amber Lin: Okay, okay, sounds good. I’ll temporarily put this on you, just to keep track, and then… We’ll remember.
44 00:07:55.830 ⇒ 00:07:57.190 Amber Lin: No.
45 00:07:58.690 ⇒ 00:07:59.770 Amber Lin: Here we go.
46 00:08:00.110 ⇒ 00:08:04.300 Amber Lin: Okay, I’ll skip his other tests for now.
47 00:08:08.270 ⇒ 00:08:09.400 Amber Lin: Alright.
48 00:08:10.450 ⇒ 00:08:11.490 Amber Lin: Okay.
49 00:08:11.880 ⇒ 00:08:14.619 Amber Lin: And then, Henry, on your side.
50 00:08:15.510 ⇒ 00:08:18.450 Amber Lin: Let’s see… .
51 00:08:18.450 ⇒ 00:08:27.370 Henry Zhao: Yeah, so my two main priorities are the ones that are due today. I’m talking to Zora to get him to book those meetings. He should have already done… he did his thumbs up already.
52 00:08:27.890 ⇒ 00:08:28.750 Henry Zhao: He hasn’t told me.
53 00:08:28.750 ⇒ 00:08:29.140 Amber Lin: anything.
54 00:08:29.140 ⇒ 00:08:29.640 Henry Zhao: Yet.
55 00:08:30.070 ⇒ 00:08:34.590 Henry Zhao: Devon SEO dashboard, I already have everything I need, but it’s just a bigger lift.
56 00:08:34.860 ⇒ 00:08:40.649 Henry Zhao: You can see it’s 5 points, so… I don’t know if I’ll finish it today, but I already gave them a heads up that it might go into next week.
57 00:08:41.140 ⇒ 00:08:43.270 Henry Zhao: But for now, you can adjust it today.
58 00:08:43.419 ⇒ 00:08:47.780 Henry Zhao: I’ll actually maybe change Devon Eden A10 to tomorrow.
59 00:08:47.910 ⇒ 00:08:50.590 Henry Zhao: So I only got access to Search Console this morning.
60 00:08:53.190 ⇒ 00:08:55.400 Amber Lin: And this is not blocked anymore.
61 00:08:55.670 ⇒ 00:08:56.330 Henry Zhao: No.
62 00:08:58.800 ⇒ 00:09:00.440 Henry Zhao: In progress, yeah.
63 00:09:02.970 ⇒ 00:09:03.560 Amber Lin: Okay.
64 00:09:04.330 ⇒ 00:09:07.140 Henry Zhao: Webhook, and then Judd,
65 00:09:07.580 ⇒ 00:09:10.809 Henry Zhao: Cleanup attribute… wait, no. Yeah, customer I.O. dashboard…
66 00:09:12.770 ⇒ 00:09:31.400 Henry Zhao: that also is almost done. I just have some other questions for you, Awash, in terms of which model I should use for attributed revenue, so I’ll send those to you in Slack a little bit later today. I just need to gather my thoughts, Awash, but if you give me some help on that, I’d really appreciate it. Basically, what happened is I took 3 different data sources, Awash.
67 00:09:31.400 ⇒ 00:09:37.670 Henry Zhao: And they all gave me different numbers, and they all look really low, so I just want to get some help into what I might be doing well.
68 00:09:39.010 ⇒ 00:09:40.629 Awaish Kumar: Sorry, what do you need help with?
69 00:09:41.450 ⇒ 00:09:44.499 Henry Zhao: Just understanding which model to use for attributed revenue.
70 00:09:44.500 ⇒ 00:09:45.250 Awaish Kumar: terrific.
71 00:09:45.780 ⇒ 00:09:49.139 Awaish Kumar: Yeah, if you can just send me your questions, or, like…
72 00:09:49.250 ⇒ 00:09:51.920 Awaish Kumar: For what purpose do you need,
73 00:09:52.050 ⇒ 00:09:55.609 Awaish Kumar: To use the data, I can find the accessible way.
74 00:09:55.760 ⇒ 00:09:56.450 Awaish Kumar: Excuse me.
75 00:09:56.450 ⇒ 00:10:06.829 Henry Zhao: to know all the revenue that came from certain campaigns. So I checked INT North Beam Export, which you just told me about. I checked Fact Transactions, the one that’s really along with,
76 00:10:07.240 ⇒ 00:10:10.180 Henry Zhao: original at the end, and then I tried the one that’s, like, product, analytics.
77 00:10:10.180 ⇒ 00:10:13.149 Awaish Kumar: The fact transaction is the one, actually.
78 00:10:13.300 ⇒ 00:10:16.910 Awaish Kumar: Where we have all the… like, revenue.
79 00:10:17.770 ⇒ 00:10:22.320 Awaish Kumar: Coming in, right? That’s the only one from where the revenue comes.
80 00:10:22.840 ⇒ 00:10:28.829 Awaish Kumar: And if you want to tie it back to any specific product or a campaign, then you have to…
81 00:10:29.250 ⇒ 00:10:34.310 Awaish Kumar: I use the… For example, in a luteum source.
82 00:10:34.480 ⇒ 00:10:38.349 Awaish Kumar: field, which is also available in Fact Transactional.
83 00:10:39.340 ⇒ 00:10:43.009 Henry Zhao: Yeah, it just doesn’t match with the other tables, which, if you tell me that those are not.
84 00:10:43.340 ⇒ 00:10:47.419 Amber Lin: they’re not supposed to match, then that’s fine. But I just want to get an understanding of why they don’t match.
85 00:10:47.430 ⇒ 00:10:49.589 Henry Zhao: So I’ll ask you that in Slack later.
86 00:10:49.850 ⇒ 00:10:52.339 Henry Zhao: Okay, sure. Thank you.
87 00:10:52.650 ⇒ 00:10:57.720 Henry Zhao: And then Judd told me the data from Google Analytics, and it’s way higher, but that’s kind of expected, I think.
88 00:10:58.270 ⇒ 00:11:02.160 Amber Lin: But yeah, eventually these people are gonna ask me these questions, and I just want to be prepared to answer.
89 00:11:03.390 ⇒ 00:11:08.989 Henry Zhao: And then I just need to finish the webhook into Customer.io. You can mark that as for today also.
90 00:11:09.680 ⇒ 00:11:10.410 Amber Lin: Okay.
91 00:11:11.300 ⇒ 00:11:13.740 Henry Zhao: I just had to clarify some stuff with Judd last night.
92 00:11:14.170 ⇒ 00:11:16.530 Henry Zhao: We went into midnight, so I just haven’t done it yet.
93 00:11:18.450 ⇒ 00:11:22.599 Amber Lin: Yeah, and also, is there a ticket tracking in this one that you were doing?
94 00:11:22.600 ⇒ 00:11:25.819 Henry Zhao: kind of… Yeah, we have one, but I just already finished it.
95 00:11:26.660 ⇒ 00:11:28.290 Henry Zhao: It’s in the bottom floor.
96 00:11:28.290 ⇒ 00:11:30.769 Amber Lin: Oh, that one. Okay. Sounds good.
97 00:11:31.020 ⇒ 00:11:35.179 Amber Lin: Then checking on here… okay, that’s good.
98 00:11:35.390 ⇒ 00:11:38.060 Henry Zhao: And then Awash gave me feedback on my first pull request.
99 00:11:38.430 ⇒ 00:11:41.870 Henry Zhao: I just don’t have time to get it right now, because those are a little bit more complex.
100 00:11:42.170 ⇒ 00:11:46.059 Henry Zhao: So Awash, I’ll probably get to those next week, but thank you for the feedback on the pull request.
101 00:11:51.850 ⇒ 00:11:55.519 Henry Zhao: Yeah, and if Zoran doesn’t answer me about these meetings, I’m just gonna schedule them, and hope he makes it.
102 00:11:59.630 ⇒ 00:12:00.740 Henry Zhao: Wait forever, you know?
103 00:12:04.230 ⇒ 00:12:05.456 Amber Lin: And then…
104 00:12:06.430 ⇒ 00:12:09.459 Awaish Kumar: Yeah, just an update, Amber. I added to…
105 00:12:09.700 ⇒ 00:12:14.129 Awaish Kumar: more labels in linear, as AT&T.
106 00:12:14.500 ⇒ 00:12:23.820 Awaish Kumar: And one is called Data Investigations, and that’s basically whenever you create a ticket, which is related to…
107 00:12:25.180 ⇒ 00:12:33.789 Awaish Kumar: the data bugs, or data investigation, like Mithesh says, data books off, or something, like, things related to that.
108 00:12:34.060 ⇒ 00:12:36.549 Awaish Kumar: We should assign data investigation ticket.
109 00:12:36.660 ⇒ 00:12:38.310 Awaish Kumar: And what, and then…
110 00:12:38.640 ⇒ 00:12:49.790 Awaish Kumar: Then we have some requests, like, okay, pull some data for me, like, like the request from Rayan, right? Yeah. You wanted us to get some data, so this is ad hoc.
111 00:12:50.520 ⇒ 00:12:55.100 Awaish Kumar: This is ad hoc, this is because we are not fixing any bug, it’s just
112 00:12:55.450 ⇒ 00:12:59.209 Awaish Kumar: Yeah, we can get some data in a specific format.
113 00:12:59.480 ⇒ 00:13:04.240 Awaish Kumar: The other one is where we… we… our existing models
114 00:13:04.360 ⇒ 00:13:19.940 Awaish Kumar: are failing. There is, some questions on our existing models or existing dashboards. That should be just our investigation. And the third, third label is, like, it’s, it’s called, like, I don’t know.
115 00:13:20.400 ⇒ 00:13:26.470 Awaish Kumar: It’s, it’s about, like, for any new model pushes. So, for example.
116 00:13:27.150 ⇒ 00:13:29.180 Awaish Kumar: I pushed a new data model.
117 00:13:29.420 ⇒ 00:13:35.420 Awaish Kumar: And then there are… like, question, like.
118 00:13:36.940 ⇒ 00:13:41.589 Awaish Kumar: For example, questions on that model, like.
119 00:13:44.410 ⇒ 00:13:47.319 Awaish Kumar: The model is not,
120 00:13:47.800 ⇒ 00:13:57.169 Awaish Kumar: not working as expected, or there is some data issues. For a newly built model, we should…
121 00:13:57.690 ⇒ 00:14:00.770 Awaish Kumar: classify them separately. It’s called New Data Model.
122 00:14:01.400 ⇒ 00:14:03.470 Amber Lin: Mmm, okay. Is it?
123 00:14:03.470 ⇒ 00:14:04.010 Awaish Kumar: That’s true.
124 00:14:04.010 ⇒ 00:14:08.489 Amber Lin: Is it in the tags? I was trying to find it so I can start using that.
125 00:14:08.490 ⇒ 00:14:10.189 Awaish Kumar: In the labels, man.
126 00:14:10.840 ⇒ 00:14:15.350 Amber Lin: Like, so I have the investigation…
127 00:14:15.350 ⇒ 00:14:16.180 Awaish Kumar: an investigation.
128 00:14:16.180 ⇒ 00:14:24.270 Amber Lin: New model, data issues, some of these are old, so…
129 00:14:24.270 ⇒ 00:14:26.330 Awaish Kumar: I added these two today.
130 00:14:26.330 ⇒ 00:14:27.629 Amber Lin: Oh, okay, okay.
131 00:14:27.630 ⇒ 00:14:30.020 Awaish Kumar: And new model data issues. These are the…
132 00:14:30.020 ⇒ 00:14:30.659 Amber Lin: Yeah, okay.
133 00:14:30.660 ⇒ 00:14:31.879 Awaish Kumar: I’m talking about…
134 00:14:32.290 ⇒ 00:14:33.139 Amber Lin: Sounds good.
135 00:14:33.360 ⇒ 00:14:37.749 Awaish Kumar: But the ad hoc, we are already separating it, using the project, so we don’t.
136 00:14:37.750 ⇒ 00:14:38.610 Amber Lin: Yeah.
137 00:14:38.890 ⇒ 00:14:41.100 Awaish Kumar: have a label again, but for the other…
138 00:14:41.100 ⇒ 00:14:41.930 Amber Lin: Sounds good.
139 00:14:42.290 ⇒ 00:14:51.049 Awaish Kumar: For the other data I showed, for new model, or for the existing model, they should just use, like, labels to classify.
140 00:14:51.230 ⇒ 00:14:57.340 Amber Lin: So, for instance, these two would be new data, new model data issues.
141 00:14:58.190 ⇒ 00:15:05.179 Awaish Kumar: Like, this is basically a new model itself, right? The model is not even pushed, right?
142 00:15:05.610 ⇒ 00:15:06.270 Amber Lin: Yeah.
143 00:15:06.340 ⇒ 00:15:11.469 Awaish Kumar: So, when a new model comes in, and I just build a new model.
144 00:15:12.380 ⇒ 00:15:15.180 Awaish Kumar: So that’s okay, that’s… I just pushed the new order, but then…
145 00:15:15.590 ⇒ 00:15:19.279 Awaish Kumar: Then I pushed a new model, second day,
146 00:15:19.620 ⇒ 00:15:36.120 Awaish Kumar: the… one of the columns is… does not have the data as expected, or is missing some data, or there are some data issues in that model. So we get, like, following… follow-up tickets, like, if we get 3-4 more follow-up tickets on a data model, which is.
147 00:15:36.120 ⇒ 00:15:38.169 Amber Lin: I see, I see, I understand.
148 00:15:38.390 ⇒ 00:15:49.810 Awaish Kumar: So, when a data model is old enough, like, for two weeks, model was working fine. After that, it started failing, then it is just a new data… it’s just a data investigation.
149 00:15:49.930 ⇒ 00:16:00.370 Awaish Kumar: Okay. Because now the data model is mature, but when a new data model is pushed on a second date phase, then it’s basically… it was not built correctly, that means.
150 00:16:01.700 ⇒ 00:16:03.170 Amber Lin: Got it, okay.
151 00:16:03.510 ⇒ 00:16:09.869 Amber Lin: Feel free to label any of your tickets, and I’ll try to label new stuff as they come in.
152 00:16:10.390 ⇒ 00:16:16.110 Awaish Kumar: Yeah, we want to, yeah, we want to label new ones, that’s the priority. For existing ones, we’ll see, like.
153 00:16:17.110 ⇒ 00:16:24.340 Awaish Kumar: If we can, we’ll do it otherwise. We can just leave. But for the future tickets, we just want to have those tickets.
154 00:16:24.900 ⇒ 00:16:26.360 Amber Lin: Okay, got it.
155 00:16:26.880 ⇒ 00:16:29.940 Amber Lin: Alright, I think that’s a short stand-up today,
156 00:16:30.370 ⇒ 00:16:33.430 Amber Lin: I think that’s all we needed.
157 00:16:33.800 ⇒ 00:16:35.150 Awaish Kumar: Okay, thank you.
158 00:16:35.150 ⇒ 00:16:36.959 Amber Lin: Yeah. Thank you both.
159 00:16:37.620 ⇒ 00:16:38.970 Henry Zhao: Thank you, guys.